A Novel Adaptive Model Predictive Control Strategy of Solid Oxide Fuel Cell in DC Microgrids

Abstract

Solid oxide fuel cell (SOFC) becomes increasingly popular in dc microgrid applications. Controlling SOFC is challenging because the dynamics of SOFC are difficult to maintain under complex internal reactions and changing operating conditions. To solve these problems, this article proposes a novel adaptive model predictive control (AMPC) algorithm, which adopts a parameter estimator to update the system parameters online. The robustness of the proposed AMPC is investigated under different microgrid scenarios, including the overload, underload, short-circuit, and significant dc bus voltage drop situations. The proposed AMPC algorithm produces superior SOFC control performance over the conventional model predictive control (MPC), Wiener MPC, and PI and fuzzy PI controllers. Furthermore, it significantly reduces the system model dependence that is shared by nearly all the model-based SOFC control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm is validated through hardware-in-the-loop experiments under various operating conditions and system parameter variations.